---
title: "ABM"
output:
flexdashboard::flex_dashboard:
orientation: rows
vertical_layout: scroll
favicon: kth-logo.png
source_code: embed
params:
kthid: u1o2ujjd
unit_code: KTH
---
```{r setup, include=FALSE}
library(flexdashboard)
library(DT)
library(readr)
library(purrr)
library(dplyr)
library(ggplot2)
library(bibliomatrix)
library(tidyr)
library(scales)
#colors_vb <- RColorBrewer::brewer.pal(4, "Set2")
colors_vb <- palette_kth(4)
run_unit<- params$unit_code #"KTH"
use_package_data <- TRUE
# Retrieve result tables to use in tabs and key indicators
if (!use_package_data) {
df_diva <- abm_table1(unit_code = run_unit)
df_cit3y <- abm_table2(unit_code= run_unit)
df_cf <- abm_table3(unit_code= run_unit)
df_jcf <- abm_table4(unit_code= run_unit)
df_copub <- abm_table5(unit_code= run_unit)
} else {
df_diva <- pluck(abm_public_kth$units, params$unit_code, 1)
df_cit3y <- pluck(abm_public_kth$units, params$unit_code, 2)
df_cf <- pluck(abm_public_kth$units, params$unit_code, 3)
df_jcf <- pluck(abm_public_kth$units, params$unit_code, 4)
df_copub <- pluck(abm_public_kth$units, params$unit_code, 5)
}
round_df <- function(x) {
x <- x %>% mutate_if(is.numeric, function(x) round(x, digits=2))
x
}
res <- map(list(df_diva, df_cit3y, df_cf, df_jcf, df_copub), round_df)
df_diva <- res %>% pluck(1)
df_cit3y <- res %>% pluck(2)
df_cf <- res %>% pluck(3)
df_jcf <- res %>% pluck(4)
df_copub <- res %>% pluck(5)
#dfs<- abm_data(unit_code = run_unit) %>% collect()
```
Overview
===========================
Row
---------------------
### Total publications (fractionalized) {.value-box}
```{r}
total_pubs <-
df_diva %>% select("2018") %>% sum(na.rm=TRUE) %>% round(digits=1)
valueBox(
value = total_pubs,
# icon = "fa-area-chart",
color = colors_vb[1]
)
```
### Field-normalized citations {.value-box}
```{r}
avg_cf <- as.numeric(df_cf[4,"cf"]) %>% round(digits=2)
#dfs %>% pluck(1, 3) %>%
#filter(Period == "2014-2016") %>%
#pluck("cf")
valueBox(
value = avg_cf,
# icon = "fa-area-chart",
color = colors_vb[2]
)
```
### Share top 10% publications {.value-box}
```{r}
topten <- as.numeric(df_cf[4,"top10_share"]) %>% percent()
valueBox(
value = topten,
# icon = "fa-area-chart",
color = colors_vb[2]
)
```
### Journal citations (field normalized) {.value-box}
```{r}
jcf <- as.numeric(df_jcf[4,"jcf"]) %>% round(digits=2)
valueBox(
value = jcf,
# icon = "fa-area-chart",
color = colors_vb[3]
)
```
### Share top 20% journals {.value-box}
```{r}
jcf <- as.numeric(df_jcf[5,"top20_share"]) %>% percent()
valueBox(
value = jcf,
# icon = "fa-area-chart",
color = colors_vb[3]
)
```
Row
---------------------
### Co-publications with Swedish non-universities
```{r}
# copub_nonuni_swe <- as.numeric(df_copub[5,"nonuniv_share"]) %>% percent()
#
# valueBox(
# value = copub_nonuni_swe,
# # icon = "fa-area-chart",
# color = colors_vb[4]
# )
abm_waffle_copub_nonuniv(df_copub)
```
### International co-publications
```{r}
abm_waffle_copub_int(df_copub)
# copub_int <- as.numeric(df_copub[5,"int_share"]) %>% percent()
# # dfs %>% pluck(1, 5) %>%
# # filter(Period == "2015-2017") %>%
# # pluck("International Share")
#
# valueBox(
# value = copub_int,
# # icon = "fa-area-chart",
# color = colors_vb[4]
# )
```
### Background
The bibliometric indicators referred are based on publications registered in DiVA and published 2011 to 2017. Only publications which have been affiliated to KTH are included. This yields that publications written by a researcher before she/he was employed at KTH, and that are not affiliated to KTH, are not included in the statistics.
Statistics regarding citations and co-publishing are based on the subset of publications in DiVA that are registered in Web of Science.
### Further information
- [Guide to the Annual Bibliometric Monitoring at KTH](https://intra.kth.se/bibliometri/public/link/attachment/Guide%20to%20the%20Annual%20Bibliometric%20Monitoring%20at%20KTH.pdf)
- [Description of data, methods and indicators in KTH Annual Bibliometric Monitoring](https://intra.kth.se/bibliometri/public/link/attachment/Description%20of%20data,%20methods%20and%20indicators%20in%20KTH%20Annual%20Bibliometric%20Monitoring.pdf)
- [Formal definitions of field normalized citation indicators at KTH](https://www.kth.se/polopoly_fs/1.544479!/Formal%20definitions%20of%20field%20normalized%20citation%20indicators%20at%20KTH.pdf)
- [Information about DiVA and the registration process - Handle publications in DiVA](https://www.kth.se/en/biblioteket/publicera-analysera/hantera-publikationer)
- [President decision about the Annual Bibliometric Monitoring](https://intra.kth.se/bibliometri/public/link/attachment/Beslut%200934%20Arlig%20bibliometrisk%20uppfoljning.pdf)
Row
----------------------
### Publication data and attributions
```{r, echo=FALSE, out.height="60px"}
# embed_data <- function(path)
# paste0("data:", mime::guess_type(path), ";base64,", base64enc::base64encode(path))
#
# embed_file_link <- function(path,
# href = embed_data(path),
# name = basename(path),
# text = paste("Download", name), ...) {
# htmltools::a(text, href = href, download = name, ...)
# }
#
# icon_download <- htmltools::tag("i", list(class = "fa fa-download"))
# icon_download <- htmltools::attachDependencies(icon_download,
# flexdashboard:::html_dependencies_fonts(TRUE, FALSE))
#
# # export to xlsx format, into tempdir so we can embed a file link
#
# # data that we want to export
# publications_kth <- abm_publications(unit_code = run_unit)
#
# #install.packages("openxlsx")
# library(openxlsx)
# excel_file <- file.path(tempdir(), "PublList_KTH.xlsx")
# write.xlsx(publications_kth, excel_file)
#
# embed_file_link(excel_file,
# title = "Download Publication List in Excel format",
# text = icon_download, class="btn btn-primary")
```
Publication list for KTH in Excel format
Certain data included herein is derived from the Science Citation Index Expended (SCIE), Social Sciences Citation Index (SSCI), Arts & Humanities Citation Index (AHCI), Conference Proceedings Citation Index - Sciences (CPCI-S) and Conference Proceedings Citation Index - Social Sciences & Humanities (CPCI -SSH), prepared by Clarivate Analytics, Philadelphia, Pennsylvania, USA: : © Copyright Clarivate Analytics. 2017. All rights reserved.
Publications in DiVA
=====================================
### Publication volume, fractionalized
```{r}
# All main tables are now retrieved at the top, and later used in tabs
#------------------------------------------
#my_locale <- readr::locale(decimal_mark = ".")
#my_parse_guess <- function(x)
# readr::parse_guess(x, locale = my_locale, guess_integer = TRUE)
#
# df <-
# dfs %>% pluck(1, 1) %>% select(-c(1, 2)) %>%
# mutate_all(my_parse_guess)
# %>%
# select(`WoS Coverage` = WoS_coverage, `Publication Type` = Publication_Type_DiVA) %>%
# mutate(Total = 0)
DT::datatable(df_diva, rownames = FALSE, options = list(
bPaginate = FALSE,
pageLength = 100,
dom = 't'
))
```
Row
---------------------
### Publication volume
```{r, fig.width=10, fig.height=5}
abm_graph_diva(df_diva)
```
### Web of Science coverage
```{r, fig.width=8, fig.height=5}
abm_graph_wos_coverage(df_diva)
```
Citation impact
=====================================
Row
---------------------
### Citations 3-year window
```{r}
# dfs %>% pluck(1, 2) %>% select(-c(1, 2)) %>%
# mutate_all(my_parse_guess)
DT::datatable(df_cit3y, rownames = FALSE, options = list(
bPaginate = FALSE,
dom = 't'
))
```
### Field normalized citations - Fractionalized (3-year moving average)
```{r}
# dfs %>% pluck(1, 3) %>% select(-c(1, 2)) %>%
# mutate_all(my_parse_guess)
DT::datatable(df_cf, rownames = FALSE, options = list(
bPaginate = FALSE,
dom = 't'
))
```
Row
---------------------
### Average field normalized citation indicator
```{r}
abm_graph_cf(df_cf)
```
### %Top-10%
```{r}
abm_graph_top10(df_cf)
```
Journal impact
=====================================
Row
---------------------
### 3-year moving average
```{r}
# dfs %>% pluck(1, 4) %>% select(-c(1, 2)) %>% mutate_all(my_parse_guess)
DT::datatable(df_jcf, rownames = FALSE, options = list(
bPaginate = FALSE,
dom = 't'
))
```
Row
---------------------
### Field normalized journal indicator
```{r}
abm_graph_jcf(df_jcf)
```
### Journal Top-20%
```{r}
abm_graph_top20(df_jcf)
```
Co-publishing
=====================================
Row
---------------------
### Internationally and with Swedish non-university organizations (3-year moving average)
```{r}
# dfs %>% pluck(1, 5) %>% select(-c(1, 2)) %>% mutate_all(my_parse_guess)
DT::datatable(df_copub, rownames = FALSE, options = list(
bPaginate = FALSE,
dom = 't'
))
```
Row
----------------------
### Graphs
```{r, fig.width=9, fig.height=6}
abm_graph_copub(df_copub)
```
```{r}
# library(tidyr)
# library(ggthemes)
# library(plotly)
#
#
# ts <-
# df %>%
# select(-c(`WoS Coverage`, Total)) %>%
# gather(key = "Year", value = "Indicator", 2:8)
#
# p <-
# ggplot(
# data = ts,
# aes(x = Year, y = Indicator, group = Publication,
# colour = Publication, label = Publication)) +
# geom_line() +
# geom_point() +
# scale_color_brewer(type = "qual", palette = "Paired") +
# geom_text(data = ts %>% filter(Year == 2017) %>% arrange(desc(Indicator)) %>% slice(1:3),
# aes(label = Publication), hjust = 1,
# vjust = 2) +
# theme_economist_white() +
# theme(
# legend.position = "none",
# axis.title.x = element_blank(),
# axis.title.y = element_blank())
#
# ggplotly(p)
```
